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mmagic.evaluation.functional.gaussian_funcs 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import cv2
import numpy as np


[文档]def gaussian(x, sigma): """Gaussian function. Args: x (array_like): The independent variable. sigma (float): Standard deviation of the gaussian function. Return: np.ndarray or scalar: Gaussian value of `x`. """ return np.exp(-x**2 / (2 * sigma**2)) / (sigma * np.sqrt(2 * np.pi))
[文档]def dgaussian(x, sigma): """Gradient of gaussian. Args: x (array_like): The independent variable. sigma (float): Standard deviation of the gaussian function. Return: np.ndarray or scalar: Gradient of gaussian of `x`. """ return -x * gaussian(x, sigma) / sigma**2
[文档]def gauss_filter(sigma, epsilon=1e-2): """Gradient of gaussian. Args: sigma (float): Standard deviation of the gaussian kernel. epsilon (float): Small value used when calculating kernel size. Default: 1e-2. Return: filter_x (np.ndarray): Gaussian filter along x axis. filter_y (np.ndarray): Gaussian filter along y axis. """ half_size = np.ceil( sigma * np.sqrt(-2 * np.log(np.sqrt(2 * np.pi) * sigma * epsilon))) size = int(2 * half_size + 1) # create filter in x axis filter_x = np.zeros((size, size)) for i in range(size): for j in range(size): filter_x[i, j] = gaussian(i - half_size, sigma) * dgaussian( j - half_size, sigma) # normalize filter norm = np.sqrt((filter_x**2).sum()) filter_x = filter_x / norm filter_y = np.transpose(filter_x) return filter_x, filter_y
[文档]def gauss_gradient(img, sigma): """Gaussian gradient. From https://www.mathworks.com/matlabcentral/mlc-downloads/downloads/ submissions/8060/versions/2/previews/gaussgradient/gaussgradient.m/ index.html Args: img (np.ndarray): Input image. sigma (float): Standard deviation of the gaussian kernel. Return: np.ndarray: Gaussian gradient of input `img`. """ filter_x, filter_y = gauss_filter(sigma) img_filtered_x = cv2.filter2D( img, -1, filter_x, borderType=cv2.BORDER_REPLICATE) img_filtered_y = cv2.filter2D( img, -1, filter_y, borderType=cv2.BORDER_REPLICATE) return np.sqrt(img_filtered_x**2 + img_filtered_y**2)
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